System and method for contactless blood pressure determination

ABSTRACT

A system and method for contactless blood pressure determination. The method includes: receiving a captured image sequence; determining, using a trained hemoglobin concentration (HC) changes machine learning model, bit values from a set of bitplanes in the captured image sequence that represent the HC changes of the subject; determining a blood flow data signal; extracting one or more domain knowledge signals associated with the determination of blood pressure; building a trained blood pressure machine learning model with a blood pressure training set, the blood pressure training set including the blood flow data signal of the one or more predetermined ROIs and the one or more domain knowledge signals; determining, using the blood pressure machine learning model trained with a blood pressure training set, an estimation of blood pressure; and outputting the determination of blood pressure.

TECHNICAL FIELD

The following relates generally to detection of a human blood pressureand more specifically to a system and method for contactless human bloodpressure determination, via a data-driven and machine learning approach.

BACKGROUND

Measurement of blood pressure is the primary approach used to diagnoseconditions such as hypertension. Conventionally, arterial pressures ofthe human circulatory system are measured through invasive means, forexample, by penetrating the skin and taking pressure measurements fromwithin the blood vessels, such as with intra-arterial techniques; or bynon-invasive means, which provide an estimate of the actual pressure.The former approach is typically restricted to highly qualified andtrained staff that monitor arterial lines on patients at intensive carecentres within a hospital setting. The latter approach typicallyincludes non-invasive techniques seen in general practice for routineexaminations and monitoring. An exemplary arterial pressure waveformsignal measured from an inter-arterial blood pressure monitor, includingsome of the associated features of the signal, is shown in FIG. 7.

As an example, two currently popular conventional approaches forconducting Non-Invasive Blood Pressure (NIBP) measurements both requiredirect physical contact to be established between an instrument and ahuman subject.

One of the conventional approaches, an auscultatory approach, uses astethoscope and a sphygmomanometer. This approach includes an inflatablecuff placed around the upper arm at roughly the same vertical height asthe heart, attached to a mercury or aneroid manometer.

The second of the conventional approaches, an oscillometric approach, isfunctionally similar to that of the auscultatory method, but with anelectronic pressure sensor (transducer) fitted in the cuff to detectblood flow, instead of using the stethoscope and an expert's judgment.In practice, the pressure sensor is a calibrated electronic device witha numerical readout of blood pressure. To maintain accuracy, calibrationmust be checked periodically, unlike with the mercury manometer. In mostcases, the cuff is inflated and released by an electrically operatedpump and valve, which may be fitted on the wrist (elevated to heartheight) or other area. The oscillometric method can vary widely inaccuracy, and typically needs to be checked at specified intervals, andif necessary recalibrated.

Thus, conventional approaches require close access and direct physicalcontact with a human subject's body, typically with the arm of thesubject. This contact requires that the subject is compliant and awarethat a blood pressure measurement is underway. As an example, to acquirea subject's blood pressure, they must have knowledge of the measurementand be physically collocated with the NIBP instrument.

SUMMARY

In an aspect, there is provided a method for contactless blood pressuredetermination of a human subject, the method executed on one or moreprocessors, the method comprising: receiving a captured image sequenceof light re-emitted from the skin of one or more humans; determining,using a trained hemoglobin concentration (HC) changes machine learningmodel trained with a HC changes training set, bit values from a set ofbitplanes in the captured image sequence that represent the HC changesof the subject, the HC changes training set comprising the capturedimage sequence; determining a blood flow data signal of one or morepredetermined regions of interest (ROIs) of the subject captured on theimages based on the bit values from the set of bitplanes that representthe HC changes; extracting one or more domain knowledge signalsassociated with the determination of blood pressure from the blood flowdata signal of each of the ROIs; building a trained blood pressuremachine learning model with a blood pressure training set, the bloodpressure training set comprising the blood flow data signal of the oneor more predetermined ROIs and the one or more domain knowledge signals;determining, using the blood pressure machine learning model trainedwith the blood pressure training set, an estimation of blood pressurefor the human subject; and outputting the determination of bloodpressure.

In a particular case, determining the estimation of blood pressurecomprises determining an estimation of systolic blood pressure (SBP) anddiastolic blood pressure (DBP).

In another case, the set of bitplanes in the captured image sequencethat represent the HC changes of the subject are the bitplanes that aredetermined to significantly increase a signal-to-noise ratio (SNR).

In yet another case, the method further comprising preprocessing theblood flow data signal with a Butterworth filter or a Chebyshev filter.

In yet another case, extracting the one or more domain knowledge signalscomprises determining a magnitude profile of the blood flow data signalof each of the ROIs.

In yet another case, determining the magnitude profile comprises usingdigital filters to create a plurality of frequency filtered signals ofthe blood flow data signal in the time-domain for each image in thecaptured image sequence.

In yet another case, extracting the one or more domain knowledge signalscomprises determining a phase profile of the blood flow data signal ofeach of the ROIs.

In yet another case, determining the phase profile comprises: applying amultiplier junction to the phase profile to generate a multiplied phaseprofile; and applying a low pass filter to the multiplied phase profileto generate a filtered phase profile.

In yet another case, determining the phase profile comprises determininga beat profile, the beat profile comprising a plurality of beat signalsbased on a Doppler or an interference effect.

In yet another case, extracting the one or more domain knowledge signalscomprises determining at least one of systolic uptake, peak systolicpressure, systolic decline, dicrotic notch, and diastolic runoff of theblood flow data signal of each of the ROIs.

In yet another case, extracting the one or more domain knowledge signalscomprises determining waveform morphology features of the blood flowdata signal of each of the ROIs.

In yet another case, extracting the one or more domain knowledge signalscomprises determining one or more biosignals, the biosignals comprisingat least one of heart rate measured from the human subject, Mayer wavesmeasured from the human subject, and breathing rates measured from thehuman subject.

In yet another case, the method further comprising receiving groundtruth blood pressure data, and wherein the blood pressure training setfurther comprises the ground truth blood pressure data.

In yet another case, the ground truth blood pressure data comprises atleast one of an intra-arterial blood pressure measurement of the humansubject, an auscultatory measurement of the human subject, or anoscillometric measurement of the human subject.

In yet another case, the method further comprising applying a pluralityof band-pass filters, each having a separate passband, to each of theblood flow data signals to produce a bandpass filter (BPF) signal setfor each ROI, and wherein the blood pressure training set comprising theBPF signal set for each ROI.

In another aspect, there is provided a system for contactless bloodpressure determination of a human subject, the system comprising one ormore processors and a data storage device, the one or more processorsconfigured to execute: a transdermal optical imaging (TOI) module toreceive a captured image sequence of light re-emitted from the skin ofone or more humans, the TOI module determines, using a trainedhemoglobin concentration (HC) changes machine learning model trainedwith a HC changes training set, bit values from a set of bitplanes inthe captured image sequence that represent the HC changes of thesubject, the HC changes training set comprising the captured imagesequence, the TOI module determines a blood flow data signal of one ormore predetermined regions of interest (ROIs) of the subject captured onthe images based on the bit values from the set of bitplanes thatrepresent the HC changes; a profile module to extract one or more domainknowledge signals associated with the determination of blood pressurefrom the blood flow data signal of each of the ROIs; a machine learningmodule to build a trained blood pressure machine learning model with ablood pressure training set, the blood pressure training set comprisingthe blood flow data signal of the one or more predetermined ROIs and theone or more domain knowledge signals, the machine learning moduledetermines, using the blood pressure machine learning model trained witha blood pressure training set, an estimation of blood pressure of thehuman subject; and an output module to output the determination of bloodpressure.

In a particular case, determination of the estimation of blood pressureby the machine learning module comprises determining an estimation ofsystolic blood pressure (SBP) and diastolic blood pressure (DBP).

In another case, the set of bitplanes in the captured image sequencethat represent the HC changes of the subject are the bitplanes that aredetermined to significantly increase a signal-to-noise ratio (SNR).

In yet another case, the system further comprising a filter module topreprocess the blood flow data signal with a Butterworth filter or aChebyshev filter.

In yet another case, extracting the one or more domain knowledge signalsby the profile module comprises determining a magnitude profile of theblood flow data signal of each of the ROIs.

In yet another case, determining the magnitude profile by the profilemodule comprises using digital filters to create a plurality offrequency filtered signals of the blood flow data signal in thetime-domain for each image in the captured image sequence.

In yet another case, extracting the one or more domain knowledge signalsby the profile module comprises determining a phase profile of the bloodflow data signal of each of the ROIs.

In yet another case, determining the phase profile by the profile modulecomprises: applying a multiplier junction to the phase profile togenerate a multiplied phase profile; and applying a low pass filter tothe multiplied phase profile to generate a filtered phase profile.

In yet another case, determining the phase profile by the profile modulecomprises determining a beat profile, the beat profile comprising aplurality of beat signals based on a Doppler or an interference effect.

In yet another case, extracting the one or more domain knowledge signalsby the profile module comprises determining at least one of systolicuptake, peak systolic pressure, systolic decline, dicrotic notch, anddiastolic runoff of the blood flow data signal of each of the ROIs.

In yet another case, extracting the one or more domain knowledge signalsby the profile module comprises determining waveform morphology featuresof the blood flow data signal of each of the ROIs.

In yet another case, extracting the one or more domain knowledge signalsby the profile module comprises determining one or more biosignals, thebiosignals comprising at least one of heart rate measured from the humansubject, Mayer waves measured from the human subject, and breathingrates measured from the human subject.

In yet another case, the profile module receives ground truth bloodpressure data, and wherein the blood pressure training set furthercomprises the ground truth blood pressure data.

In yet another case, the ground truth blood pressure data comprises atleast one of an intra-arterial blood pressure measurement of the humansubject, an auscultatory measurement of the human subject, or anoscillometric measurement of the human subject.

In yet another case, the system further comprising a filter module toapply a plurality of band-pass filters, each having a separate passband,to each of the blood flow data signals to produce a bandpass filter(BPF) signal set for each ROI, and wherein the blood pressure trainingset comprising the BPF signal set for each ROI.

These and other aspects are contemplated and described herein. It willbe appreciated that the foregoing summary sets out representativeaspects of systems and methods for the determination of blood pressureto assist skilled readers in understanding the following detaileddescription.

BRIEF DESCRIPTION OF THE DRAWINGS

The features of the invention will become more apparent in the followingdetailed description in which reference is made to the appended drawingswherein:

FIG. 1 is an block diagram of a system for contactless blood pressuredetermination, according to an embodiment;

FIG. 2 is a flowchart for a method for contactless blood pressuredetermination, according to an embodiment;

FIG. 3 illustrates re-emission of light from skin epidermal andsubdermal layers;

FIG. 4 is a set of surface and corresponding transdermal imagesillustrating change in hemoglobin concentration for a particular humansubject at a particular point in time;

FIG. 5 is a diagrammatic representation of a memory cell;

FIG. 6 is graph illustrating an exemplary TOI signal generated by thesystem of FIG. 1;

FIG. 7 is a graph illustrating an exemplary arterial pressure signalfrom a typical inter-arterial blood pressure monitor;

FIG. 8 is a diagrammatic block illustration of the system of FIG. 1; and

FIG. 9 is an illustration of bitplanes for a three channel image.

DETAILED DESCRIPTION

Embodiments will now be described with reference to the figures. Forsimplicity and clarity of illustration, where considered appropriate,reference numerals may be repeated among the Figures to indicatecorresponding or analogous elements. In addition, numerous specificdetails are set forth in order to provide a thorough understanding ofthe embodiments described herein. However, it will be understood bythose of ordinary skill in the art that the embodiments described hereinmay be practiced without these specific details. In other instances,well-known methods, procedures and components have not been described indetail so as not to obscure the embodiments described herein. Also, thedescription is not to be considered as limiting the scope of theembodiments described herein.

Various terms used throughout the present description may be read andunderstood as follows, unless the context indicates otherwise: “or” asused throughout is inclusive, as though written “and/or”; singulararticles and pronouns as used throughout include their plural forms, andvice versa; similarly, gendered pronouns include their counterpartpronouns so that pronouns should not be understood as limiting anythingdescribed herein to use, implementation, performance, etc. by a singlegender; “exemplary” should be understood as “illustrative” or“exemplifying” and not necessarily as “preferred” over otherembodiments. Further definitions for terms may be set out herein; thesemay apply to prior and subsequent instances of those terms, as will beunderstood from a reading of the present description.

Any module, unit, component, server, computer, terminal, engine ordevice exemplified herein that executes instructions may include orotherwise have access to computer readable media such as storage media,computer storage media, or data storage devices (removable and/ornon-removable) such as, for example, magnetic disks, optical disks, ortape. Computer storage media may include volatile and non-volatile,removable and non-removable media implemented in any method ortechnology for storage of information, such as computer readableinstructions, data structures, program modules, or other data. Examplesof computer storage media include RAM, ROM, EEPROM, flash memory orother memory technology, CD-ROM, digital versatile disks (DVD) or otheroptical storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, or any other medium which canbe used to store the desired information and which can be accessed by anapplication, module, or both. Any such computer storage media may bepart of the device or accessible or connectable thereto. Further, unlessthe context clearly indicates otherwise, any processor or controller setout herein may be implemented as a singular processor or as a pluralityof processors. The plurality of processors may be arrayed ordistributed, and any processing function referred to herein may becarried out by one or by a plurality of processors, even though a singleprocessor may be exemplified. Any method, application or module hereindescribed may be implemented using computer readable/executableinstructions that may be stored or otherwise held by such computerreadable media and executed by the one or more processors.

The following relates generally to detection of human blood pressure andmore specifically to a system and method for contactless human bloodpressure determination, via a data-driven and machine learning approach.

In embodiments of the system and method described herein, technicalapproaches are provided to solve the technological problem ofdetermining human blood pressure without having to contact a humansubject by the blood pressure measurement instrument. Blood pressure isdetermined using image processing techniques performed over a pluralityof images captured by one or more digital imaging cameras, such as adigital video camera.

The technical approaches described herein offer the substantialadvantages of not requiring direct physical contact between a subjectand a blood pressure measurement instrument. As an example of asubstantial advantage using the technical approaches described herein,remote blood pressure measurement can be performed on a subject using asuitable imaging device, such as by a video camera communicating over acommunications channel. As another example of a substantial advantageusing the technical approaches described herein, blood pressuremeasurements can be determined from previously recorded video material.

The technical approaches described herein also offer the substantialadvantages of not requiring periodic recalibration or certification ofan instrument. The system and method described herein advantageously donot make use of any moving components such as a pump or an expanding armcuff and bladder, which typically require recalibration orcertification.

The technical approaches described herein advantageously utilize bodyspecific data driven machine-trained models that are executed against anincoming video stream. In some cases, the incoming video stream are aseries of images of the subject's facial area. In other cases, theincoming video stream can be a series of images of any body extremitywith exposed vascular surface area; for example, the subject's palm. Inmost cases, each captured body extremity requires separately trainedmodels. For the purposes of the following disclosure, reference will bemade to capturing the subject's face with the camera; however, it willbe noted that other areas can be used with the techniques describedherein.

Referring now to FIG. 1, a system for contactless blood pressuredetermination 100 is shown. The system 100 includes a processing unit108, one or more video-cameras 103, a storage device 101, and an outputdevice 102. The processing unit 108 may be communicatively linked to thestorage device 101, which may be preloaded, periodically loaded, and/orcontinuously loaded with video imaging data obtained from one or morevideo-cameras 103. The processing unit 108 includes variousinterconnected elements and modules, including a TOI module 110, amachine learning module 112, a signal processing module 114, a firstfilter module 116, a combination module 118, a profile module 120, amultiplier module 122, and an output module 126. The TOI module includesan image processing unit 104. The video images captured by thevideo-camera 103 can be processed by the image processing unit 104 andstored on the storage device 101. In further embodiments, one or more ofthe modules can be executed on separate processing units or devices,including the video-camera 103 or output device 102. In furtherembodiments, some of the features of the modules may be combined or runon other modules as required.

In some cases, the processing unit 108 can be located on a computingdevice that is remote from the one or more video-cameras 103 and/or theoutput device 102, and linked over an appropriate networkingarchitecture; for example, a local-area network (LAN), a wide-areanetwork (WAN), the Internet, or the like. In some cases, the processingunit 108 can be executed on a centralized computer server, such as inoff-line batch processing.

The term “video”, as used herein, can include sets of still images.Thus, “video camera” can include a camera that captures a sequence ofstill images and “imaging camera” can include a camera that captures aseries of images representing a video stream.

Using transdermal optical imaging (TOI), the TOI module 110 can isolatehemoglobin concentration (HC) from raw images taken from the digitalcamera 103. Referring now to FIG. 3, a diagram illustrating there-emission of light from skin is shown. Light 301 travels beneath theskin 302, and re-emits 303 after travelling through different skintissues. The re-emitted light 303 may then be captured by opticalcameras 103. The dominant chromophores affecting the re-emitted lightare melanin and hemoglobin. Since melanin and hemoglobin have differentcolor signatures, it has been found that it is possible to obtain imagesmainly reflecting HC under the epidermis as shown in FIG. 4.

Using transdermal optical imaging (TOI), the TOI module 110, via theimage processing unit 104, obtains each captured image in a videostream, from the camera 103, and performs operations upon the image togenerate a corresponding optimized hemoglobin concentration (HC) imageof the subject. From the HC data, the facial blood flow localized volumeconcentrations can be determined. The image processing unit 104 isolatesHC in the captured video sequence. In an exemplary embodiment, theimages of the subject's faces are taken at 30 frames per second using adigital camera 103. It will be appreciated that this process may beperformed with alternative digital cameras, lighting conditions, andframe rates.

In a particular case, isolating HC can be accomplished by analyzingbitplanes in the sequence of video images to determine and isolate a setof the bitplanes that approximately maximize signal to noise ratio(SNR). The determination of high SNR bitplanes is made with reference toa first training set of images constituting the captured video sequence,in conjunction with blood pressure data gathered from the humansubjects. The determination of high SNR bitplanes is made with referenceto an HC training set constituting the captured video sequence. In somecases, this data is supplied along with other devices, for example, EKG,pneumatic respiration, blood pressure, laser Doppler data, or the like,collected from the human subjects, and received by the profile module120, in order to provide ground truth to train the training set for HCchange determination. A blood pressure training data set can consist ofblood pressure data obtained from human subjects by using one or moreblood pressure measurement devices as ground truth data; for example, anintra-arterial blood pressure measurement approach, an auscultatoryapproach, or an oscillometric approach. The selection of the trainingdata set based on one of these three exemplary approaches depends on asetting in which the contactless blood pressure measurement system isused; as an example, if the human subject is in a hospital intensivecare setting, the training data can be received from an intra-arterialblood pressure measurement approach.

Bitplanes are a fundamental aspect of digital images. Typically, adigital image consists of certain number of pixels (for example, awidth×height of 1920×1080 pixels). Each pixel of the digital imagehaving one or more channels (for example, color channels red, green, andblue (RGB)). Each channel having a dynamic range, typically 8 bits perpixel per channel, but occasionally 10 bits per pixel per channel forhigh dynamic range images. Whereby, an array of such bits makes up whatis known as the bitplane. In an example, for each image of color videos,there can be three channels (for example, red, green, and blue (RGB))with 8 bits per channel. Thus, for each pixel of a color image, thereare typically 24 layers with 1 bit per layer. A bitplane in such a caseis a view of a single 1-bit map of a particular layer of the imageacross all pixels. For this type of color image, there are thereforetypically 24 bitplanes (i.e., a 1-bit image per plane). Hence, for a1-second color video with 30 frames per second, there are at least 720(30×24) bitplanes. FIG. 9 is an exemplary illustration of bitplanes fora three-channel image (an image having red, green and blue (RGB)channels). Each stack of layers is multiplied for each channel of theimage; for example, as illustrated, there is a stack of bitplanes foreach channel in an RGB image. In the embodiments described herein,Applicant recognized the advantages of using bit values for thebitplanes rather than using, for example, merely the averaged values foreach channel. Thus, a greater level of accuracy can be achieved formaking predictions of HC changes, and thus blood pressure measurementsas disclosed herein, and as described for making predictions.Particularly, a greater accuracy is possible because employing bitplanesprovides a greater data basis for training the machine learning model.

TOI signals can be taken from regions of interest (ROIs) of the humansubject, for example forehead, nose, and cheeks, and can be definedmanually or automatically for the video images. The ROIs are preferablynon-overlapping. These ROIs are preferably selected on the basis ofwhich HC is particularly indicative of blood pressure measurement. Usingthe native images that consist of all bitplanes of all three R, G, Bchannels, signals that change over a particular time period (forexample, 10 seconds) on each of the ROIs are extracted.

The raw signals can be pre-processed using one or more filters by thefilter module 116, depending on the signal characteristics. Such filtersmay include, for example, a Butterworth filter, a Chebyshev filter, orthe like. Using the filtered signals from two or more ROIs, machinelearning is employed to systematically identify bitplanes that willsignificantly increase the signal differentiation (for example, wherethe SNR improvement is greater than 0.1 db) and bitplanes that willcontribute nothing or decrease the signal differentiation. Afterdiscarding the latter, the remaining bitplane images can optimallydetermine blood flow generally associated with a determination ofsystolic and diastolic blood pressure.

Machine learning approaches (such as a Long Short Term Memory (LSTM)neural network, or a suitable alternative such as non-linear SupportVector Machine) and deep learning may be used to assess the existence ofcommon spatial-temporal patterns of hemoglobin changes across subjects.The machine learning process involves manipulating the bitplane vectors(for example, 24 bitplanes×30 fps) using the bit value in each pixel ofeach bitplane along the temporal dimension. In one embodiment, thisprocess requires subtraction and addition of each bitplane to maximizethe signal differences in all ROIs over the time period. In some cases,to obtain reliable and robust computational models, the entire datasetcan be divided into three sets: the training set (for example, 80% ofthe whole subject data), the test set (for example, 10% of the wholesubject data), and the external validation set (for example, 10% of thewhole subject data). The time period can vary depending on the length ofthe raw data (for example, 15 seconds, 60 seconds, or 120 seconds). Theaddition or subtraction can be performed in a pixel-wise manner. Anexisting machine learning algorithm, the Long Short Term Memory (LSTM)neural network, or a suitable alternative thereto is used to efficientlyand obtain information about the improvement of differentiation in termsof accuracy, which bitplane(s) contributes the best information, andwhich does not in terms of feature selection. The Long Short Term Memory(LSTM) neural network allow us to perform group feature selections andclassifications. The LSTM machine learning algorithm are discussed inmore detail below. From this process, the set of bitplanes to beisolated from image sequences to reflect temporal changes in HC isobtained for determination of blood pressure.

To extract facial blood flow data, facial HC change data on each pixelof each subject's face image is extracted as a function of time when thesubject is being viewed by the camera 103. In some cases, to increasesignal-to-noise ratio (SNR), the subject's face can be divided into aplurality of regions of interest (ROIs). The division can be accordingto, for example, the subject's differential underlying physiology, suchas by the autonomic nervous system (ANS) regulatory mechanisms. In thisway, data in each ROI can be averaged. The ROIs can be manually selectedor automatically detected with the use of a face tracking software. Themachine learning module 112 can then average the data in each ROI. Thisinformation can then form the basis for the training set. As an example,the system 100 can monitor stationary HC changes contained by a selectedROI over time, by observing (or graphing) the resulting temporal profile(for example, shape) of the selected ROI HC intensity values over time.In some cases, the system 100 can monitor more complex migrating HCchanges across multiple ROIs by observing (or graphing) the spatialdispersion (HC distribution between ROIs) as it evolves over time.

Thus, it is possible to obtain a video sequence of any subject and applythe HC extracted from selected bitplanes to the computational models todetermine blood flow generally associated with systolic and diastolicblood pressure. For long running video streams with changes in bloodflow and intensity fluctuations, changes of the estimation and intensityscores over time relying on HC data based on a moving time window (e.g.,10 seconds) may be reported.

In an example using the Long Short Term Memory (LSTM) neural network,the LSTM neural network comprises at least three layers of cells. Thefirst layer is an input layer, which accepts the input data. The second(and perhaps additional) layer is a hidden layer, which is composed ofmemory cells (see FIG. 5). The final layer is output layer, whichgenerates the output value based on the hidden layer using LogisticRegression.

Each memory cell, as illustrated, comprises four main elements: an inputgate, a neuron with a self-recurrent connection (a connection toitself), a forget gate and an output gate. The self-recurrent connectionhas a weight of 1.0 and ensures that, barring any outside interference,the state of a memory cell can remain constant from one time step toanother. The gates serve to modulate the interactions between the memorycell itself and its environment. The input gate permits or prevents anincoming signal to alter the state of the memory cell. On the otherhand, the output gate can permit or prevent the state of the memory cellto have an effect on other neurons. Finally, the forget gate canmodulate the memory cell's self-recurrent connection, permitting thecell to remember or forget its previous state, as needed.

The equations below describe how a layer of memory cells is updated atevery time step t. In these equations:

x_(t) is the input array to the memory cell layer at time t. In ourapplication, this is the blood flow signal at all ROIs{right arrow over (x)} _(t)=[x _(1t) x _(2t) . . . x _(nt)]

-   -   W_(i), W_(f), W_(c), W_(o), U_(i), U_(f), U_(c), U_(o) and V_(o)        are weight matrices; and    -   b_(i), b_(f), b_(c), and b_(o) are bias vectors

First, we compute the values for i_(t), the input gate, and {tilde over(C)}_(t) the candidate value for the states of the memory cells at timet:i _(t)=σ(W _(i) x _(t) +U _(i) h _(t-1) +b _(i)){tilde over (C)} _(t)=tan h(W _(c) x _(t) +U _(c) h _(t-1) +b _(c))

Second, we compute the value for f_(t), the activation of the memorycells' forget gates at time t:f _(t)=σ(W _(f) x _(t) +U _(f) h _(t-1) +b _(f))

Given the value of the input gate activation i_(t), the forget gateactivation f_(t) and the candidate state value {tilde over (C)}_(t), wecan compute C_(t) the memory cells' new state at time t:C _(t) =i _(t) *{tilde over (C)} _(t) +f _(t) *C _(t-1)

With the new state of the memory cells, we can compute the value oftheir output gates and, subsequently, their outputs:o _(t)=σ(W _(o) x _(t) +U _(o) h _(t-1) +V _(o) C _(t) +b _(o))h _(t) =o _(t)*tan h(C _(t))

Based on the model of memory cells, for the blood flow distribution ateach time step, we can calculate the output from memory cells. Thus,from an input sequence x₀, x₁, x₂, . . . , x_(n), the memory cells inthe LSTM layer will produce a representation sequence h₀, h₁, h₂, . . ., h_(n).

The goal is to classify the sequence into different conditions. TheLogistic Regression output layer generates the probability of eachcondition based on the representation sequence from the LSTM hiddenlayer. The vector of the probabilities at time step t can be calculatedby:p _(t)=softmax(W _(output) h _(t) +b _(output))where W_(output) is the weight matrix from the hidden layer to theoutput layer, and b_(output) is the bias vector of the output layer. Thecondition with the maximum accumulated probability will be the predictedcondition of this sequence.

The machine learning module 112 uses the dynamic changes, over time, oflocalized blood-flow localized volume concentrations at each of theregions-of-interest (ROI) determined by the TOI module 110 to determineblood pressure. The blood pressure measurement approach, used by themachine learning module 112 on the HC change data from the TOI module110, utilizes a priori generation of specific machine trainedcomputational models combined with the continuous real-time extractionof features from the dynamic observed behaviour of a subject's measuredblood flow to produce a predictive estimate of the subject's bloodpressure.

The iterative process of machine learning, by the machine learningmodule 112, allows for the generation of probabilistic mappings ormulti-dimensional transfer-functions between the extracted bio-signalspresented as training input, as described herein, and the resultantsystolic blood pressure (SBP) and diastolic blood pressure (DBP)estimates as the outputs. To train the machine learning module 112,systematic collection of TOI data from a plurality of human subjects,who preferably meet certain stratification criteria for the specificpopulation study, is utilized.

During a machine training cycle, by the machine learning module 112, TOIvideos of a plurality of subjects are collected under controlledcircumstances and with accompanying “ground truth” information alongside(as described herein). Preferably, the plurality of subjects cover adiverse spectrum of ages, genders, ethnicities, pregnancy, and the like.Preferably, the plurality of subjects have a variety of blood-pressureconditions, from hypotensive to hypertensive. The machine learningmodels can be trained with increasing robustness as the diversity of thesubjects' increases.

The machine learning models are generated according to a supervisedtraining process, where the “ground truth” blood pressure, for bothsystolic and diastolic data-points, are labelled as a target conditionand a variety of training examples are presented in rounds. The trainingexamples are prepared from the subject dataset by the techniquesdescribed herein. These techniques utilize advanced data-science machinelearning architectures such as Multi-Level Perceptron and Deep(hierarchical) Neural Networks, which are capable of ‘deciphering’non-obvious relationships from large datasets to make predictiveoutcomes. In some cases, the accuracy of the blood pressure estimatesfrom such models is linearly proportional to the quantity and quality ofthe training dataset.

Turning to FIG. 2, a flowchart for a method for contactless bloodpressure determination 200 is shown.

In some cases, for increasing the accuracy of the machine learning modelregarding the relationships between blood flow data (as input) and bloodpressure estimates (as output), and for reducing the time to arrive attraining convergence, the method 200 can leverage domain knowledge toenhance the quality of the input data. Such domain knowledge can includecertain attributes, qualities or features of the input data, collectedby the profile module 120, that can be consequential to increasing theaccuracy of the relationship between the input and the output; forexample, systolic rising time, amplitude of systolic peak, amplitude ofdicrotic notch, dicrotic notch time, and pulse pressure. Extracting suchdomain knowledge from the input data and providing it into the machinelearning model during an iterative training process, the training of themachine learning model can be exaggerated by the certain attributes,qualities or features, such that the accuracy of the machine learningtraining can benefit from the inclusion of the domain knowledge. Atblock 202, facial blood flow is extracted from the video usingtransdermal optical imaging by the TOI module 110, as described herein,for localized volume concentrations at defined regions-of-interest (ROI)on the face. In addition, the TOI module 110 records dynamic changes ofsuch localized volume concentrations over time.

In an example, the face can be divided into ‘m’ different regions ofinterest. In this case, there will be ‘m’ separate ROI signals, eachprocessing a unique signal extracted from the facial image. The groupingof these ‘m’ ROI signals is collectively referred to as a bank of ROIsignals.

FIG. 6 illustrates an exemplary signal, measured as a function of time,outputted by the TOI module 110 for a particular ROI. As shown,Applicant advantageously recognized that the signal extracted from theTOI module at least partially resembles an exemplary signal taken froman inter-arterial blood pressure monitor, as shown in FIG. 7. In thiscase, while the TOI signal may be somewhat noisier than the signalextracted from the inter-arterial blood pressure monitor, the pertinentcharacteristics of the signal can be extracted and thus used to trainthe machine learning model; for example, characteristics like systolicuptake 602, peak systolic pressure 604, systolic decline 606, dictroticnotch 608, and diastolic runoff 610.

At block 204, the blood-flow volume data from each ROI is processed bythe signal processing module 114. The blood-flow volume data from eachROI can be treated as an independent signal and routed through acorresponding processing path. In this way, multiple ROIs each generatesignals which are independently, yet concurrently, processed by thesignal processing module 114 using digital signal processing (DSP)techniques. The TOI module 110 generates quantity ‘m’ uniquely definedROIs superimposed over the facial image, whose boundaries are preferablynon-overlapping in area. In other cases, the ROI boundaries may beoverlapping.

At block 206, the filter module 116 analyzes ‘n’ separately definedfrequency passbands over the image frequency spectrum received from thesignal processing module 114. The spectral energy within each passbandis measured by utilizing a narrowband digital filter with ‘bandpass’(BPF) characteristics. Each of the resultant bandpass signals is calleda “BPF signal” or “BPF instance”. In this way, each bandpass filterimplements a passband consisting of crisply defined lower and upperfrequency specification, where a gain (within the passband range) ispreferably much greater than a provided attenuation (outside thepassband range).

The filter module 116 constructs each BPF signal as an individual 12thorder Elliptical digital filter. Each filter preferably has identicalbandpass start/stop and gain/attenuation characteristics, but differingin configured start/stop ‘edge’ frequencies. The filter module 116advantageously uses this high-order filter architecture to balance therequirements for a steep roll-off magnitude characteristic with minimalphase distortion. In some cases, the passband ‘start’ frequency isconfigurable. In some cases, the passband range (span) is fixed forevery BPF at 0.1 Hz; as an example, meaning that the ‘end’ frequencywill be calculated as the ‘start’ frequency plus 0.1 Hz.

In some cases, at block 208, the combination module 118 combines a setof ‘n’ discrete BPF instances. In this way, a large contiguous frequencyrange can be covered by assigning stepwise increasing ‘start’frequencies to each BPF instance. Each BPF signal can thus operate on aportion of the facial image available frequency spectrum. Deployment ofprogressive assignments for the BPF ‘start’ frequencies can ensureapproximately complete coverage of the spectrum; as an example, between0.1 Hz and 6.0 Hz, with a granularity of 0.1 Hz, yielding a total of 60BPF instances.

Each ROI signal, of quantity ‘m’ in total, will have a locallydesignated BPF set, of quantity ‘n’ BPF signals in total, to divide andprocess the frequency spectrum of the ROI signal, as described above.This aggregation of narrowband filters is collectively referred to asthe “filter bank”.

In some cases, at block 210, the profile module 120 decomposes the ROIsignals, acquired across multiple ROIs, to generate a multi-dimensionalfrequency profile (also called a magnitude profile) and a phase profile(also called a timing profile or velocity profile). The magnitudeprofile and the timing profile are used as features (input) to themachine learning model by the machine learning module 112. This “featureengineering” can advantageously be used to enhance the effectiveness ofthe machine learning training process by increasing the useful inputdata for differentiating blood pressure determinations; and thus, have ahigher accuracy at estimating blood pressure.

In the present embodiment, domain knowledge determined by the profilemodule 120 can include the magnitude profile to enhance an attribute ofthe blood flow input data. In the case of the magnitude profile, adistribution of frequency information across the blood flow data (perROI) has been determined by the Applicant to have significance to theestimation of the blood pressure values. As such, as described below, afrequency spectrum analysis per ROI, in this case using fixed banks ofdigital filters, is performed. The digital filters' signals provide areal-time frequency spectrum of the time-domain signal; comparable toperforming fast Fourier transform (FFT) but on every frame. An intendedadvantage of using digital filters is to create ‘n’ individual frequencyfiltered streams that can be manipulated and/or routed independently tobuild the machine learning model. The analysis is thus then provided tothe machine learning model to enhance the accuracy of estimating theblood pressure output values.

In the present embodiment, domain knowledge determined by the profilemodule 120 can also include the velocity or speed of the blood-flowinput data, provided to the machine learning model, for enhancing theaccuracy of estimating the blood pressure output values. In a certaincase, a beat profile, comprising a collection of beat signals, can beused to quantify the velocity of the blood-flow input data. Beat signalsare a motion detection technique based on the Doppler or interferenceeffect. Two beat signals of exactly the same frequency will havezero-hertz (difference) beat signal when multiplied. The frequency ofthe beat signal is linearly proportional to the ‘difference’ between thetwo fundamental signals. In this way, when two arbitrary signals arereceived and multiplied, the resulting signal will be the difference(subtraction) of the two input frequencies. This difference infrequencies can then be converted to a motion or velocity.

As described, a beat signal can be used to derive an indication ofmotion of one ROI blood flow signal relative to another ROI blood flowsignal; where the frequency of the resultant beat signal is proportionalto a difference in blood flow velocity (known as the heterodyne effect).A beat vector can be created for each ROI against some or all of theother ROIs (eliminating any redundant pairs); whereby this collection ofbeat vectors can be considered the timing profile. In some cases, thetiming profile can be constantly updated at fixed intervals. As such,the timing profile can represent an overall complex interference patternwhich is based on the differences in blood flow velocities. Thereforethe timing profile can be provided to the machine learning model toemphasize blood flow velocity in order to enhance the accuracy ofestimating the blood pressure output values.

The magnitude profile includes ‘n’ discrete points which span the rangefrom the low to the high end of the analyzed spectrum. The magnitudeprofile is generated by the profile module 120 by creating a singlesumming junction F(i), where T represents a frequency step or positionalindex for summation of quantity ‘m’ total BPF outputs associated withthe frequency step T. Each magnitude point, F(i) represents a measure ofthe narrowband spectral energy summed across ‘m’ separate ROIs.

The profile module 120 constructs the timing profile ‘P’ from quantity‘s’ slices, with each P(s) slice representing the sum of all possiblepair combinations of quantity ‘m’ total BPF outputs associated with thefrequency step ‘i’. In some cases, the potential pairings are reduced toeliminate redundant combinations.

In some cases, at block 212, the pair combinations, or remaining uniquepair combinations, are routed to a multiplier module 122, representing amultiplier junction at index ‘k’, to create a new ‘hetrodyne’ outputsignal H(i, k), which is determined via multiplication of signals fromdifferent inputs. For each frequency step ‘i’, the ‘k’ index will rangethrough ((m×(m−1))/2) total junctions. P(s) therefore represents thesummation of H(i,k) for a given step T. There are quantity ‘n’ slices ofoutput signals H(i, k) in total to cover the entire spectrum of BPFfilters.

In some cases, at block 214, the filter module 116 further processes the‘P’ profile by a low pass filter (LPF). In this way, the filter module116 can remove the sidebands created in the heterodyne alterations whileproviding a quantifying measure to the ‘beat’ signal energy resultingfrom the signal pairings.

In some cases, the machine learning module 112 can utilize selectiveconfigurations, such as those configured by a trainer, of the temporal(time changing) features provided by the magnitude profile and thefrequency profile to create individually trained model(s), eachemphasizing different training characteristics. As described herein,these numerically derived features can also be combined with one or morephysiological biosignals that are determined from the TOI blood-flowdata; for example, heart-rate, Mayer wave, respiration or breathingcycle, other low or ultra-low frequency arterial oscillations which arenaturally occurring and continuously present within the subject, and thelike.

Both the features outputted by the filter module 116 and the recoveredbiosignals (physiological) from the TOI blood-flow can be utilizedduring the a priori machine training process, as described above, aswell as in a posteriori blood pressure estimation, as described herein.

At block 216, the output module 126 outputs, via the trained models ofthe machine learning module 112, the estimates of systolic bloodpressure (SBP) and diastolic blood pressure (DBP) to the output device102. In some cases, the output module 126, at block 218, canadditionally output supplementary outputs to the output device 102. Insome cases, the supplementary outputs can be estimated outputs of a mean(average) SBP and a mean (average) DBP. In some cases, the supplementaryand independent output can be a pulse pressure (PP) being the differencebetween SBP and DBP. As an example, these supplementary output valuesmay be used to provide validation points (or limits) for dynamic shiftsin the estimates of systolic blood pressure (SBP) and diastolic bloodpressure (DBP); such as to differentiate between rapid (acute) changesin the subject's blood pressure versus longer term (chronic) bloodpressure measurements.

Accordingly, the method for contactless blood pressure determination 200uses machine learning to determine estimates of SBP and DBP. The machinelearning approach, described herein, of iterative training ‘encodes’ thecomplex relationships between the blood flow raw data inputs and theestimated blood pressure outputs. The encoding is of multiple vectors ofweights corresponding to the coefficients of salient multi-dimensionaltransfer functions.

The machine trained models, described herein, use training examples thatcomprise known inputs (for example, TOI blood flow data) and knownoutputs (ground truths) of SBP and DBP values. The relationship beingapproximated by the machine learning model is TOI blood-flow data to SBPand DBP estimates; whereby this relationship is generally complex andmulti-dimensional. Through iterative machine learning training, such arelationship can be outputted as vectors of weights and/or coefficients.The trained machine learning model being capable of using such vectorsfor approximating the input and output relationship between TOI bloodflow input and blood pressure estimated output.

In the machine learning models, the magnitude profile F(i) transformsthe TOI input data stream into frequency domain values, while (in somecases, concurrently) the timing profile P(i) transforms the same TOIinput data stream into a difference, or ‘beat’, signals between pairs ofdata streams. In some cases, the magnitude profile F(i) can be generated(transformed) by digital filter banks. In this case, TOI time-seriesinput signals are received and an output is generated into separatefrequency ‘bins’. The above is referred to as a transform because it iscomparable in effect to executing a Fast-Fourier-Transform (FFT) onevery single frame. This approach is advantageous because it is muchsimpler to execute time-domain digital filters, in addition to the factthat it is possible to manipulate or route each output streamindependently. In other cases, instead of digital filter banks, themagnitude profile F(i) can be generated using a hardware implementation;for example, using a hardware based field-programmable gate array (FPGA)FFT module. In some cases, the per frame output from a bank of digitalfilters is comparable to the per frame FFT output of the same digitalinput signal.

The frequency domain values and the beat signals can be provided to themachine learning model to further refine the model and therefore provideenhanced accuracy for estimating the SBP and DBP.

FIG. 8 illustrates a exemplary diagram of the embodiments describedherein. The TOI module 110 receives a set of images 802 of the humansubject from a camera. Using machine learning models, the TOI module 110performs bitplane analysis 804 on the set of images 802 to arrive at TOIsignals 806 for each ROI. In some cases, in order to increase accuracyof the blood pressure determination, the TOI module 110 can performfeature extraction 808 on each of the TOI signals for each ROI to feedinto the machine learning model, as described herein. Feature extraction808 can include, for example, determining waveform morphology featuresof the signals; such as, horizontal (time) and vertical (HC) features ofthe waves, derivatives of the signals, or the like. Feature extraction808 can also include, for example, determining frequency domain featuresof the signals; such as, magnitude and phase of a Fourier series of thesignals, or the like. Feature extraction 808 can also include, forexample, determining physiological biosignal features of the signals;such as, heart rate, Mayer wave, breathing, or the like. Featureextraction 808 can also include, for example, determining blood-flowvelocity based on the signals. In some cases, demographics 810 (forexample, gender, age, height, weight, or the like) of the human subjectscan be used to inform the feature extraction 808. A machine learningmodel can then be trained 812 by the machine learning module 112 basedon the bitplane data per ROI 806, in some cases in conjunction with thefeature extraction 808, to determine blood pressure data. The machinelearning model can be, for example, a convolutional neural network(CNN), a deep neural network (DNN), a multilayer perceptron (MLP), orthe like. In some cases, the accuracy of the training can be aided byground truth data 814; such as, systolic/diastolic blood pressuremeasured on the human training subjects using, for example, aninter-arterial blood pressure monitor. Using the trained machinelearning model, blood pressure can be determined for a particular humansubject 816.

In further embodiments, optical sensors pointing, or directly attachedto the skin of any body parts such as for example the wrist or forehead,in the form of a wrist watch, wrist band, hand band, clothing, footwear,glasses or steering wheel may be used. From these body areas, the systemmay also extract blood flow data for determination of blood pressure.

In still further embodiments, the system may be installed in robots andtheir variables (e.g., androids, humanoids) that interact with humans toenable the robots to detect blood pressure on the face or other-bodyparts of humans whom the robots are interacting with. Thus, the robotsequipped with transdermal optical imaging capacities read the humans'blood pressure to enhance machine-human interaction.

The foregoing system and method may be applied to a plurality of fields.In one embodiment the system may be installed in a smartphone device toallow a user of the smartphone to measure their blood pressure. Inanother embodiment, the system may be provided in a video camera locatedin a hospital room to allow the hospital staff to monitor the bloodpressure of a patient without causing the patient discomfort by havingto attach a device to the patient.

Further embodiments can be used in police stations and border stationsto monitor the blood pressure of suspects during interrogation. In yetfurther embodiments, the system can be used in marketing to see theblood pressure changes of consumers when confronted with specificconsumer goods.

Other applications may become apparent.

Although the invention has been described with reference to certainspecific embodiments, various modifications thereof will be apparent tothose skilled in the art without departing from the spirit and scope ofthe invention as outlined in the claims appended hereto. The entiredisclosures of all references recited above are incorporated herein byreference.

The invention claimed is:
 1. A method for contactless blood pressuredetermination of a human subject, the method executed on one or moreprocessors, the method comprising: receiving a captured image sequenceof light re-emitted from the skin of one or more humans; determining,using a trained hemoglobin concentration (HC) changes machine learningmodel trained with a HC changes training set, bit values from a set ofbitplanes in the captured image sequence that represent the HC changesof the subject, the HC changes training set comprising the capturedimage sequence; determining a blood flow data signal of one or morepredetermined regions of interest (ROIs) of the subject captured on theimages based on the bit values from the set of bitplanes that representthe HC changes; extracting one or more domain knowledge signalsassociated with the determination of blood pressure from the blood flowdata signal of each of the ROIs, wherein extracting the one or moredomain knowledge signals comprises determining a phase profile of theblood flow data signal of each of the ROIs, determining the phaseprofile comprises applying a multiplier junction to the phase profile togenerate a multiplied phase profile and applying a low pass filter tothe multiplied phase profile to generate a filtered phase profile;building a trained blood pressure machine learning model with a bloodpressure training set, the blood pressure training set comprising theblood flow data signal of the one or more predetermined ROIs and the oneor more domain knowledge signals; determining, using the blood pressuremachine learning model trained with the blood pressure training set, anestimation of blood pressure for the human subject; and outputting thedetermination of blood pressure.
 2. The method of claim 1, whereindetermining the estimation of blood pressure comprises determining anestimation of systolic blood pressure (SBP) and diastolic blood pressure(DBP).
 3. The method of claim 1, wherein the set of bitplanes in thecaptured image sequence that represent the HC changes of the subject arethe bitplanes that are determined to significantly increase asignal-to-noise ratio (SNR).
 4. The method of claim 1, furthercomprising preprocessing the blood flow data signal with a Butterworthfilter or a Chebyshev filter.
 5. The method of claim 1, whereinextracting the one or more domain knowledge signals comprisesdetermining a magnitude profile of the blood flow data signal of each ofthe ROIs.
 6. The method of claim 5, wherein determining the magnitudeprofile comprises using digital filters to create a plurality offrequency filtered signals of the blood flow data signal in thetime-domain for each image in the captured image sequence.
 7. The methodof claim 1, wherein determining the phase profile further comprisesdetermining a beat profile, the beat profile comprising a plurality ofbeat signals based on a Doppler or an interference effect.
 8. The methodof claim 1, wherein extracting the one or more domain knowledge signalscomprises determining at least one of systolic uptake, peak systolicpressure, systolic decline, dicrotic notch, pulse pressure, anddiastolic runoff of the blood flow data signal of each of the ROIs. 9.The method of claim 1, wherein extracting the one or more domainknowledge signals comprises determining waveform morphology features ofthe blood flow data signal of each of the ROIs.
 10. The method of claim1, wherein extracting the one or more domain knowledge signals comprisesdetermining one or more biosignals, the biosignals comprising at leastone of heart rate measured from the human subject, Mayer waves measuredfrom the human subject, and breathing rates measured from the humansubject.
 11. The method of claim 1, further comprising receiving groundtruth blood pressure data, and wherein the blood pressure training setfurther comprises the ground truth blood pressure data.
 12. The methodof claim 11, wherein the ground truth blood pressure data comprises atleast one of an intra-arterial blood pressure measurement of the humansubject, an auscultatory measurement of the human subject, or anoscillometric measurement of the human subject.
 13. The method of claim1, further comprising applying a plurality of band-pass filters, eachhaving a separate passband, to each of the blood flow data signals toproduce a bandpass filter (BPF) signal set for each ROI, and wherein theblood pressure training set comprising the BPF signal set for each ROI.14. A system for contactless blood pressure determination of a humansubject, the system comprising one or more processors and a data storagedevice, the one or more processors configured to execute: a transdermaloptical imaging (TOI) module to receive a captured image sequence oflight re-emitted from the skin of one or more humans, the TOI moduledetermines, using a trained hemoglobin concentration (HC) changesmachine learning model trained with a HC changes training set, bitvalues from a set of bitplanes in the captured image sequence thatrepresent the HC changes of the subject, the HC changes training setcomprising the captured image sequence, the TOI module determines ablood flow data signal of one or more predetermined regions of interest(ROIs) of the subject captured on the images based on the bit valuesfrom the set of bitplanes that represent the HC changes; a profilemodule to extract one or more domain knowledge signals associated withthe determination of blood pressure from the blood flow data signal ofeach of the ROIs, wherein extracting the one or more domain knowledgesignals by the profile module comprises determining a phase profile ofthe blood flow data signal of each of the ROIs, determining the phaseprofile by the profile module comprises applying a multiplier junctionto the phase profile to generate a multiplied phase profile and applyinga low pass filter to the multiplied phase profile to generate a filteredphase profile; a machine learning module to build a trained bloodpressure machine learning model with a blood pressure training set, theblood pressure training set comprising the blood flow data signal of theone or more predetermined ROIs and the one or more domain knowledgesignals, the machine learning module determines, using the bloodpressure machine learning model trained with the blood pressure trainingset, an estimation of blood pressure of the human subject; and an outputmodule to output the determination of blood pressure.
 15. The system ofclaim 14, wherein determination of the estimation of blood pressure bythe machine learning module comprises determining an estimation ofsystolic blood pressure (SBP) and diastolic blood pressure (DBP). 16.The system of claim 14, wherein the set of bitplanes in the capturedimage sequence that represent the HC changes of the subject are thebitplanes that are determined to significantly increase asignal-to-noise ratio (SNR).
 17. The system of claim 14, furthercomprising a filter module to preprocess the blood flow data signal witha Butterworth filter or a Chebyshev filter.
 18. The system of claim 14,wherein extracting the one or more domain knowledge signals by theprofile module comprises determining a magnitude profile of the bloodflow data signal of each of the ROIs.
 19. The system of claim 18,wherein determining the magnitude profile by the profile modulecomprises using digital filters to create a plurality of frequencyfiltered signals of the blood flow data signal in the time-domain foreach image in the captured image sequence.
 20. The system of claim 14,wherein determining the phase profile by the profile module furthercomprises determining a beat profile, the beat profile comprising aplurality of beat signals based on a Doppler or an interference effect.21. The system of claim 14, wherein extracting the one or more domainknowledge signals by the profile module comprises determining at leastone of systolic uptake, peak systolic pressure, systolic decline,dicrotic notch, pulse pressure, and diastolic runoff of the blood flowdata signal of each of the ROIs.
 22. The system of claim 14, whereinextracting the one or more domain knowledge signals by the profilemodule comprises determining waveform morphology features of the bloodflow data signal of each of the ROIs.
 23. The system of claim 14,wherein extracting the one or more domain knowledge signals by theprofile module comprises determining one or more biosignals, thebiosignals comprising at least one of heart rate measured from the humansubject, Mayer waves measured from the human subject, and breathingrates measured from the human subject.
 24. The system of claim 14,wherein the profile module receives ground truth blood pressure data,and wherein the blood pressure training set further comprises the groundtruth blood pressure data.
 25. The system of claim 24, wherein theground truth blood pressure data comprises at least one of anintra-arterial blood pressure measurement of the human subject, anauscultatory measurement of the human subject, or an oscillometricmeasurement of the human subject.
 26. The system of claim 14, furthercomprising a filter module to apply a plurality of band-pass filters,each having a separate passband, to each of the blood flow data signalsto produce a bandpass filter (BPF) signal set for each ROI, and whereinthe blood pressure training set comprising the BPF signal set for eachROI.